Pattern recognition device, pattern recognition method, and computer program product

ABSTRACT

According to an embodiment, a pattern recognition device is configured to divide an input signal into a plurality of elements, convert the divided elements into feature vectors having the same dimensionality to generate a set of feature vectors, and evaluate the set of feature vectors using a recognition dictionary including models corresponding to respective classes, to output a recognition result representing a class or a set of classes to which the input signal belongs. The models each include sub-models each corresponding to one of possible division patterns in which a signal to be classified into a class corresponding to the model can be divided into a plurality of elements. A label expressing a model including a sub-model conforming to the set of feature vectors, or a set of labels expressing a set of models including sub-models conforming to the set of feature vectors is output as the recognition result.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation application of InternationalApplication No. PCT/JP2015/063522, filed May 11, 2015, the entirecontents of which are incorporated herein by reference.

FIELD

Embodiments described herein relate generally to a pattern recognitiondevice, a pattern recognition method, and a computer program product.

BACKGROUND

In a field of pattern recognition, the following two methods are knownas a method of performing pattern recognition on an input signal inwhich a separation point of a recognition unit is not clear. The firstmethod is a method of dividing the input signal into a plurality ofelements to be coupled to each other in accordance with a predeterminedstandard, and individually recognizing each element (hereinafter, thismethod is referred to as an “analytic method”). The second method is amethod of performing recognition and division at the same time whileconsidering every possibility of a division point of the input signalusing a stochastic model such as a hidden Markov model (HMM)(hereinafter, this method is referred to as an “wholistic method”).

However, in the analytic method, temporarily divided elements arecoupled to each other using a heuristic method, so that accuracy inrecognition is not sufficiently secured in some cases. On the otherhand, in the wholistic method, processing is performed while consideringevery possibility of the division point, so that a calculation amount islarge, and a high-spec hardware resource is required. As describedherein, the analytic method and the wholistic method each have adisadvantage, so that there is a demand for a novel technique in whichsuch disadvantages are solved.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a functional configurationexample of a pattern recognition device according to an embodiment;

FIG. 2 is a conceptual diagram of processing of dividing an input signalinto a plurality of elements;

FIG. 3 is a conceptual diagram of a model included in a recognitiondictionary;

FIG. 4 is a conceptual diagram of a model included in the recognitiondictionary;

FIG. 5 is a flowchart illustrating an example of a processing procedureperformed by the pattern recognition device according to the embodiment;

FIG. 6 is a conceptual diagram for explaining an analytic method in therelated art;

FIG. 7 is a conceptual diagram of a model including a noise state;

FIGS. 8A and 8B are conceptual diagrams of processing of dividing Koreancharacters into elements;

FIG. 9 is a diagram illustrating an example of division patterns of aKorean character; and

FIG. 10 is a block diagram illustrating a hardware configuration exampleof the pattern recognition device according to the embodiment.

DETAILED DESCRIPTION

According to an embodiment, a pattern recognition device includes adivision unit, a feature extracting unit, and a recognition unit. Thedivision unit is configured to divide an input signal into a pluralityof elements. The feature extracting unit is configured to convert thedivided elements into feature vectors having the same dimensionality,and generate a set of feature vectors. The recognition unit isconfigured to evaluate the set of feature vectors using a recognitiondictionary, and output a recognition result representing a class or aset of classes to which the input signal belongs. The recognitiondictionary includes models corresponding to respective classes. Themodels each include sub-models each corresponding to one of possibledivision patterns in which a signal to be classified into a classcorresponding to the model can be divided into a plurality of elements.Each sub-model has a state corresponding to each element divided basedon a division pattern corresponding to the sub-model, the state beingexpressed by a function of labels representing a feature vector and thestate. The recognition unit outputs, as the recognition result, a labelexpressing a model including a sub-model conforming to the set offeature vectors, or a set of labels expressing a set of models includingsub-models conforming to the set of feature vectors.

The following describes a pattern recognition device, a patternrecognition method, and a computer program product according to anembodiment with reference to the drawings.

The pattern recognition device according to the present embodimentperforms pattern recognition on an input signal in which a separationpoint of a recognition unit is not clear using a novel method combiningan analytic method and an wholistic method in the related art. That is,a basic concept is to divide the input signal into a plurality ofelements to obtain a set of feature vectors of the elements, and outputa recognition result representing a class or a set of classes conformingto the set of feature vectors using a stochastic model.

Typically, a way of dividing the input signal into a plurality ofelements (division pattern) is not limited to one for each class. Forexample, when the input signal is an image of a handwritten character,the image may have different forms depending on a habit and the like ofa writer, so that signals to be classified into the same class may bedivided in different division patterns. When the signals to beclassified into the same class are divided in different divisionpatterns, distribution and the number of feature vectors extracted fromrespective divided elements are largely different for each element, sothat the input signal cannot be properly recognized with a standardmodel such as a left-to-right model (refer to F. Camastra et al.“Machine Learning for Audio, Image and Video Analysis: Theory andApplications”, Springer-Verlag, 2007) in a hidden Markov model.

Thus, in the present embodiment, for each class, each possible divisionpattern of a signal to be classified into the class is assumed to be asub-model, and a model obtained by coupling all sub-models is used as amodel corresponding to the class.

FIG. 1 is a block diagram illustrating a functional configuration of thepattern recognition device according to the present embodiment. Asillustrated in FIG. 1, the pattern recognition device according to thepresent embodiment includes a signal input unit 1, a division unit 2, afeature extracting unit 3, and a recognition unit 4.

The signal input unit 1 receives an input of a signal as a recognitiontarget. The signal as a recognition target is, for example, a characteror a character string represented as an image, other images, a voicesignal represented as a waveform, and various sensor signals. To thesignal input unit 1, input is such digital information or digitalinformation on which preprocessing such as binarization is performed asneeded.

The division unit 2 divides the signal input to the signal input unit 1into a plurality of elements. Specifically, when the signal input to thesignal input unit 1 is a character string image, for example, processingperformed by the division unit 2 can be implemented by applying analysisof projection and coupling component described in A. Rosenfeld et al.,“Digital image processing” (the supervisor of a translation: MakotoNagao), Kindai kagaku sha Co., Ltd., 1978, or a method of “Division intobasic segment” described in Hiroshi Murase et al., “Segmentation andrecognition of character from handwritten character string introducinglanguage information”, IEICE academic journal (D), J69-D(9), pp.1292-1301, 1986.

FIG. 2 is a conceptual diagram of processing of dividing the inputsignal into a plurality of elements, and illustrates a state in which acharacter string image of “

” is divided into five elements by the division unit 2. In the exampleillustrated in FIG. 2, a direction of dividing the character stringimage is one direction, but the embodiment is not limited thereto. Thesignal may be divided into a plurality of elements using atwo-dimensional division pattern.

When the signal input to the signal input unit 1 is a signal representedby a time-series waveform such as a voice signal and various sensorsignals, for example, processing performed by the division unit 2 can beimplemented by applying a method of causing, to be a division point, apoint where a state in which signal power is equal to or smaller than athreshold has been continued for a certain time or more.

An order is given to each divided element. The order of each dividedelement can be determined based on coordinates in a horizontal directionin an image when an original signal is an image, and based on time whenthe original signal is a time-series waveform such as a voice signal anda sensor signal. In this case, each divided element may be caused tohave a structure such as a series, and positional information in thestructure may be given thereto. Specific examples include a method ofgiving, sequentially from the left, a number as the positionalinformation to each element into which the character string imageillustrated in FIG. 2 is divided, and a method of giving, in an order ofearlier time, a number as the positional information to each elementinto which the time-series waveform such as a voice signal and varioussensor signals is divided. In a case of treating an image in which adivision direction is not limited to one, as described later, there is amethod of previously determining, for each division pattern, a symbol ofeach element divided in the division pattern (refer to FIG. 8), andgiving the symbol as the positional information.

The feature extracting unit 3 converts respective elements divided bythe division unit 2 into feature vectors having the same dimensionality,and generates a set of feature vectors. Specifically, the featureextracting unit 3 performs, on a signal constituting each dividedelement, preprocessing such as normalizing a length and a quantizationlevel. The feature extracting unit 3 outputs, as a feature vector of theelement, a feature vector including, as a component, a value after thepreprocessing or a value after performing filter processing such asGaussian filter and conversion processing such as Fourier transformationon a signal after the preprocessing. In this case, all feature vectorsof the respective elements may be normalized so that a norm becomes 1.In this way, the feature extracting unit 3 extracts the feature vectorfrom each element one by one, and generates a set of feature vectors.

As a specific example of processing of converting the element into thefeature vector, for example, there is a method of normalizing a time ofeach element of a voice signal, extracting a feature of a Mel-FrequencyCepstrum Coefficient described in Sadaoki Furui, “New phonetics andsonics”, Kindai kagaku sha Co., Ltd., 2006, and directly arrangingvalues as feature vectors. There is also a method of extracting, fromeach element of the image, a feature of weighted direction indexhistogram described in Shinji Tsuruoka et al., “Recognition ofhandwritten kanji/hiragana using weighted direction index histogrammethod”, IEICE academic journal (D), J70-D(7), pp. 1390-1397, 1987.

The recognition unit 4 evaluates, using a recognition dictionary 10, theset of feature vectors generated by the feature extracting unit 3, andoutputs a recognition result representing a class or a set of classes towhich the signal input to the signal input unit 1 belongs.

The recognition dictionary 10 is a database including a modelcorresponding to each class treated as a classification destination ofthe signal by the pattern recognition device according to the presentembodiment, and is held inside or outside the pattern recognition deviceaccording to the present embodiment. A model of each class held by therecognition dictionary 10 is a stochastic model, and an optionalgraphical model including the hidden Markov model (refer to C. M. Bishopet al., “Pattern recognition and machine learning”, (the supervisor of atranslation: Noboru Murata), Springer Japan KK, 2007) can be used.

The recognition unit 4 seeks optimum correspondence with the set offeature vectors generated by the feature extracting unit 3 by singlyusing or combining (as described later) models included in therecognition dictionary 10. The recognition unit 4 then outputs, as arecognition result, a label expressing a model conforming to the set offeature vectors, or a set of labels expressing a set of modelsconforming to the set of feature vectors.

FIGS. 3 and 4 are conceptual diagrams of a model M included in therecognition dictionary 10. FIG. 3 is an example of the model Mcorresponding to a class into which a character image of “

” is to be classified, and FIG. 4 is an example of the model Mcorresponding to a class into which a voice signal of “toukyouto” is tobe classified.

As illustrated in FIGS. 3 and 4, the model M treated in the presentembodiment is obtained by assuming, as a sub-model m, each possibledivision pattern in which a signal to be classified into the classcorresponding to the model M can be divided into a plurality of elementsby the division unit 2, and coupling all sub-models m. A probabilitythat each sub-model m is selected is caused to match with an appearanceratio of each corresponding division pattern in learning data preparedin advance. Alternatively, prior distribution such as uniformdistribution may be given to the probability that the model M and thesub-model m are selected, and based on the prior distribution, MAPestimation (refer to C. M. Bishop et al., “Pattern recognition andmachine learning”, (the supervisor of a translation: Noboru Murata),Springer Japan KK, 2007) may be performed on the probability that themodel M and the sub-model m are selected.

The sub-model m of each model M is configured, for example, as adirected graph having, as a node, a state s corresponding to each of theelements divided based on the division pattern corresponding to thesub-model m. In this case, in the sub-model m, the state s maytransition only in one direction similarly to the left-to-right model inthe hidden Markov model. That is, the sub-model m may have aconfiguration in which the states s are linearly ordered, and transitionfrom a higher state s to a lower state s is prohibited. FIGS. 3 and 4exemplify the model M coupling the sub-models m in which the state s cantransition only in one direction. By configuring the sub-model m asdescribed above, when learning data is passed to the division unit 2 andthe class or the set of classes into which the learning data isclassified is known in advance, an assignment of each divided element toeach state s of the sub-model m can be definitely given.

Each state s of the sub-model m is expressed by a function of labelsrepresenting a feature vector and the state s. For example, thisfunction can be assumed as a probability density function of Gaussiandistribution, and a log likelihood of the feature vector can be assumedas an output of the function. In this case, as described above, by usingthe learning data, into which class or set of classes the learning datais classified being known in advance, a distribution parameter can beestimated using a method such as the EM algorithm, the variationalBayesian method, and the gradient method described in C. M. Bishop etal., “Pattern recognition and machine learning”, (the supervisor of atranslation: Noboru Murata), Springer Japan KK, 2007, for example.

The following describes a specific example of processing performed bythe recognition unit 4 using the model M as described above. Thefollowing description assumes an example in which the input signal isclassified as a series of a plurality of classes, and the same appliesto a case in which the input signal is classified into one class.

Models M are selected in an ordered manner and set as M₁, . . . , M_(p)while allowing overlapping and the number equal to or smaller than alength n of feature vector sequences (set of feature vectors) x₁, . . ., x_(n) obtained from the input signal. The sub-model m is selected fromeach model M one by one to set m₁, . . . , m_(p), and the total lengthis caused to be equal to that of a feature vector sequence. The state ofthe sub-model m thus obtained is set as s₁, . . . , s_(n), and anevaluation function of the feature vector is given by the followingexpression (1).

$\begin{matrix}{{Expression}{\mspace{11mu} \;}1} & \; \\{{f( {x_{1},\ldots \mspace{14mu},{x_{n}m_{1}},\ldots \mspace{14mu},m_{p}} )} = {{f_{1}( {M_{1},\ldots \mspace{14mu},M_{p}} )} + {f_{2}( {m_{1},\ldots \mspace{14mu},{m_{p}M_{1}},\ldots \mspace{14mu},M_{p}} )} + {\sum\limits_{j = 1}^{n}\; {f_{3}^{(j)}( {x_{j}s_{j}} )}}}} & (1)\end{matrix}$

In this case, f₁ is an evaluation function such as a probability ofN-gram (Kenji Kita, “Probabilistic language model” (Language andcomputation 5), University of Tokyo Press, 1999) determined by asequence of the model M itself, f₂ ^((i)) is an evaluation function suchas a selection probability of the sub-model m associated with selectionof a sub-model m_(i) in a model M^(i), and f₃ ^((j)) is an evaluationfunction such as a probability density function of a state s_(j)associated with assignment of a feature vector x_(j) to the state S_(j).

In this case, when the sub-model m is configured such that the state scan transition only in one direction as described above, M₁, . . . ,M_(p) and m₁, . . . , m_(p) that maximize the sub-model m can beefficiently calculated using a Viterbi algorithm as described in F.Camastra et al., “Machine Learning for Audio, Image and Video Analysis:Theory and Applications”, Springer-Verlag, 2007. Then, M₁, . . . , M_(p)that maximize the sub-model m are output as a recognition result.

The following describes an outline of processing performed by thepattern recognition device according to the present embodiment withreference to FIG. 5. FIG. 5 is a flowchart illustrating an example of aprocessing procedure performed by the pattern recognition deviceaccording to the embodiment.

First, the signal input unit 1 receives an input of a signal as arecognition target (Step S101). The signal input to the signal inputunit 1 is passed to the division unit 2.

Next, the division unit 2 receives the signal input at Step S101 fromthe signal input unit 1, and divides the signal into a plurality ofelements (Step S102). Each of the elements divided by the division unit2 is passed to the feature extracting unit 3.

Next, the feature extracting unit 3 receives each element divided atStep S102 from the division unit 2, and obtain the feature vector foreach element using the method described above to generate a set offeature vectors (Step S103). The set of feature vectors generated by thefeature extracting unit 3 is passed to the recognition unit 4.

Next, the recognition unit 4 receives the set of feature vectorsgenerated at Step S103 from the feature extracting unit 3, evaluates theset of feature vectors using the recognition dictionary 10, and outputsa recognition result representing the class or the set of classes towhich the signal input at Step S101 belongs (Step S104).

As described above with specific examples, in the present embodiment, asthe model M corresponding to each class as a classification destinationof the signal, defined is a stochastic model coupled with the sub-modelm corresponding to various division patterns of the signal to beclassified into the model M. By using the recognition dictionary 10including the model M for each class, the set of feature vectorsobtained from the input signal is evaluated, and a recognition resultrepresenting the class or the set of classes to which the input signalbelongs is output. Accordingly, with the pattern recognition deviceaccording to the present embodiment, disadvantages of the analyticmethod and the wholistic method in the related art are solved, and theinput signal in which a separation point of a recognition unit is notclear can be recognized with high accuracy with a small calculationamount.

In the analytic method in the related art, as illustrated in FIG. 6, theinput signal is divided into a plurality of elements, which are thencoupled to each other in a unit of a recognition target (in the exampleof FIG. 6, a character) to be recognized through pattern matching andthe like. The elements are coupled to each other using a heuristicmethod such that a separation point is determined assuming an averagesize of a character, for example. Thus, in the example of FIG. 6, forexample, an element of A and an element of B, and an element of C and anelement of D may be regarded as one recognition target to be processed,and accuracy in recognition cannot be sufficiently secured in this case.On the other hand, the pattern recognition device according to thepresent embodiment uses the model M that is a stochastic modelcorresponding to each class treated as a classification destination ofthe signal, searches for the model M or a set of models M conforming tothe set of feature vectors obtained from the input signal, and outputs arecognition result representing the class or the set of classes to whichthe input signal belongs, so that the input signal in which a separationpoint of a recognition unit is not clear can be recognized with highaccuracy.

In the wholistic method in the related art, pattern recognition isperformed on the input signal using the stochastic model such as thehidden Markov model. In this method, processing is performed consideringvarious possibilities of the division point, so that a calculationamount is large, and a high-spec hardware resource is required. On theother hand, the pattern recognition device according to the presentembodiment uses the model M coupled with the sub-model m correspondingto a division pattern assumed in advance for each class, searches forthe model M or a set of models M conforming to the set of featurevectors obtained from the input signal, and outputs a recognition resultrepresenting the class or the set of classes to which the input signalbelongs, so that recognition can be performed with a small calculationamount.

As described above, with the pattern recognition device according to thepresent embodiment, disadvantages of the analytic method and thewholistic method in the related art are solved, and the input signal inwhich a separation point of a recognition unit is not clear can berecognized with high accuracy with a small calculation amount.

The recognition dictionary 10 used in the present embodiment may includea reject model not corresponding to any class as a classificationdestination of the signal. As the reject model, for example, one modelobtained by extracting only the sub-model m as part of another model M,or a model obtained by coupling models the parameter values of which arerandomly determined can be used. In this case, for example, when thereject model is included in M₁, . . . , M_(p) in the expression (1)described above, the recognition unit 4 treats this as a reject, andoutputs information indicating that the recognition result cannot beobtained. Due to this, in a case in which the input signal itself is notcorrect such as a case in which an image of an erroneous handwrittencharacter is input, a user can be notified that the input signal is notcorrect.

Assuming that noise included in the input signal is erroneously treatedas one element, a model (noise model) including only one sub-modelhaving only one state may be provided as one of reject models asdescribed above, and an evaluation function corresponding to the onlyone state in the noise model may return a value 0 or a parameter valueof which may be randomly determined.

As illustrated in FIG. 7, at least one of the models M included in therecognition dictionary 10 may be configured to have a noise state s′ asa state not corresponding to any of the elements included in the signalto be classified into the class corresponding to the model M. Byconfiguring the model M as described above, even when the noise includedin the input signal is erroneously treated as one element, the elementis correctly recognized as noise, and deterioration in recognitionaccuracy due to mixing of noise can be effectively suppressed.

In the above description, mainly assumed is a case in which a divisiondirection in dividing the input signal into elements is one direction.Even when the division direction is not one direction, the sameprocessing can be performed so long as the division pattern thereof isdetermined in advance. For example, a Korean character and the like arepreferably divided into elements using a two-dimensional divisionpattern, and the pattern recognition device according to the presentembodiment can be effectively applied to recognition of such a Koreancharacter.

FIGS. 8A and 8B are conceptual diagrams of processing of dividing Koreancharacters into elements. When an image of a Korean character string isinput as a signal as a recognition target, for example, as illustratedin FIG. 8A, the image of the character string is firstly divided incharacter units by projection in a vertical direction. An operation ofperforming coupling component analysis for each character and selectingone group of adjacent coupling components to be integrated in thevertical direction is repeated until the components match with any ofdivision patterns determined in advance as illustrated in FIG. 8B. Asymbol of each element determined in advance for each division patternis then given as positional information for each of the elementsobtained in the above operation.

As illustrated in FIG. 9, the leftmost character in the Korean characterstring illustrated in FIG. 8A may be divided in division patternsindicated by 0, 2, and 4 among division patterns illustrated in FIG. 8B.Thus, the model M corresponding to the class into which this characteris to be classified is assumed to be obtained by coupling sub-models mcorresponding to the respective division patterns illustrated in FIG. 9.Due to this, pattern recognition can be performed with high accuracyusing a method similar to the method described above.

As illustrated in FIG. 10 for example, the pattern recognition deviceaccording to the present embodiment may employ a hardware configurationutilizing a typical computer including a processor such as a centralprocessing unit (CPU) 101, a storage device such as a read only memory(ROM) 102 and a random access memory (RAM) 103, an auxiliary storagedevice such as a hard disk drive (HDD) 104, a communication I/F 105 tobe connected to a network to perform communication, and a bus 106 forconnecting respective components. In this case, each functionalcomponent described above can be implemented by executing apredetermined pattern recognition program on the computer.

This pattern recognition program is recorded and provided as a computerprogram product in a computer-readable recording medium such as acompact disc read only memory (CD-ROM), a flexible disk (FD), a compactdisc recordable (CD-R), and a digital versatile disc (DVD), as aninstallable or executable file.

This pattern recognition program may be stored in another computerconnected to a network such as the Internet and provided by beingdownloaded via the network. Furthermore, this pattern recognitionprogram may be provided or distributed via a network such as theInternet.

This pattern recognition program may be embedded and provided in a ROM102, for example.

This pattern recognition program has a module configuration includingprocessing units of the pattern recognition device according to thepresent embodiment (the signal input unit 1, the division unit 2, thefeature extracting unit 3, and the recognition unit 4). As actualhardware, for example, when the CPU 101 (processor) reads the computerprogram from the recording medium to be executed, the processing unitsdescribed above are loaded into the RAM 103 (main memory), and theprocessing units described above are generated on the RAM 103 (mainmemory). Part or all of the processing units of the pattern recognitiondevice according to the present embodiment can be implemented usingdedicated hardware such as an application specific integrated circuit(ASIC) or a field-programmable gate array (FPGA).

While certain embodiments have been described, these embodiments havebeen presented by way of example only, and are not intended to limit thescope of the inventions. Indeed, the novel embodiments described hereinmay be embodied in a variety of other forms; furthermore, variousomissions, substitutions and changes in the form of the embodimentsdescribed herein may be made without departing from the spirit of theinventions. The accompanying claims and their equivalents are intendedto cover such forms or modifications as would fall within the scope andspirit of the inventions.

What is claimed is:
 1. A pattern recognition device comprising: adivision unit configured to divide an input signal into a plurality ofelements; a feature extracting unit configured to convert the dividedelements into feature vectors having the same dimensionality, andgenerate a set of feature vectors; and a recognition unit configured toevaluate the set of feature vectors using a recognition dictionary, andoutput a recognition result representing a class or a set of classes towhich the input signal belongs, wherein the recognition dictionaryincludes models corresponding to respective classes, the models eachinclude sub-models each corresponding to one of possible divisionpatterns in which a signal to be classified into a class correspondingto the model can be divided into a plurality of elements, each sub-modelhas a state corresponding to each element divided based on a divisionpattern corresponding to the sub-model, the state being expressed by afunction of labels representing a feature vector and the state, and therecognition unit outputs, as the recognition result, a label expressinga model including a sub-model conforming to the set of feature vectors,or a set of labels expressing a set of models including sub-modelsconforming to the set of feature vectors.
 2. The device according toclaim 1, wherein, in the sub-model, each state is linearly ordered, andtransition from a higher state to a lower state is prohibited.
 3. Thedevice according to claim 1, wherein the recognition dictionary includesa reject model as a model not corresponding to any class, and therecognition unit outputs information indicating that the recognitionresult is not obtained when the set of feature vectors conforms to thereject model.
 4. The device according to claim 1, wherein at least oneof the models included in the recognition dictionary further has a noisestate as a state not corresponding to any element included in a signalto be classified into a class corresponding to the model.
 5. The deviceaccording to claim 1, wherein, in a state of a sub-model, a functionheld by the state is determined based on a set of feature vectors to bean input to the state when a signal serving as learning data is input tothe pattern recognition device, and a probability of input.
 6. A patternrecognition method executed by a pattern recognition device, comprising:dividing an input signal into a plurality of elements by the patternrecognition device; converting the divided elements into feature vectorshaving the same dimensionality, and generating a set of feature vectorsby the pattern recognition device; and evaluating the set of featurevectors using a recognition dictionary, and outputting a recognitionresult representing a class or a set of classes to which the inputsignal belongs by the pattern recognition device, wherein therecognition dictionary includes models corresponding to respectiveclasses, the models each include sub-models each corresponding to one ofpossible division patterns in which a signal to be classified into aclass corresponding to the model can be divided into a plurality ofelements, each sub-model has a state corresponding to each elementdivided based on a division pattern corresponding to the sub-model, thestate being expressed by a function of labels representing a featurevector and the state, and a label expressing a model including asub-model conforming to the set of feature vectors, or a set of labelsexpressing a set of models including sub-models conforming to the set offeature vectors is output as the recognition result at the outputting.7. A computer program product comprising a computer-readable mediumincluding programmed instructions, the instructions causing a computerto implement: a function of a division unit configured to divide aninput signal into a plurality of elements; a function of a featureextracting unit configured to convert the divided elements into featurevectors having the same dimensionality, and generate a set of featurevectors; and a function of a recognition unit configured to evaluate theset of feature vectors using a recognition dictionary, and output arecognition result representing a class or a set of classes to which theinput signal belongs, wherein the recognition dictionary includes modelscorresponding to respective classes, the models each include sub-modelseach corresponding to one of possible division patterns in which asignal to be classified into a class corresponding to the model can bedivided into a plurality of elements, each sub-model has a statecorresponding to each element divided based on a division patterncorresponding to the sub-model, the state being expressed by a functionof labels representing a feature vector and the state, and therecognition unit outputs, as the recognition result, a label expressinga model including a sub-model conforming to the set of feature vectors,or a set of labels expressing a set of models including sub-modelsconforming to the set of feature vectors.